Beyond the Pipeline: Analyzing Key Factors in End-to-End Deep Learning for Historical Writer Identification

2025-10-21

AI Summary

Historical handwritten word recognition (HWI) faces challenges including high intra-class variability in handwriting styles, document degradation, and scarcity of labeled data; existing end-to-end deep learning methods exhibit limited generalization under document-level zero-shot settings. This paper systematically investigates key architectural and preprocessing factors affecting HWI model performance—specifically image pre-processing, backbone network design, text segmentation, block sampling, and feature aggregation. Experimental analysis reveals that most configurations fail due to insufficient modeling of low-level visual features. In contrast, a structurally simplified end-to-end architecture achieves state-of-the-art performance without requiring author-specific priors or complex pre-processing. This finding underscores the critical importance of robust low-level feature learning in HWI and establishes a new design paradigm for lightweight, generalizable historical document analysis systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Other Foundations of Computer Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

This paper investigates various factors that influence the performance of end-to-end deep learning approaches for historical writer identification (HWI), a task that remains challenging due to the diversity of handwriting styles, document degradation, and the limited number of labelled samples per writer. These conditions often make accurate recognition difficult, even for human experts. Traditional HWI methods typically rely on handcrafted image processing and clustering techniques, which tend to perform well on small and carefully curated datasets. In contrast, end-to-end pipelines aim to automate the process by learning features directly from document images. However, our experiments show that many of these models struggle to generalise in more realistic, document-level settings, especially under zero-shot scenarios where writers in the test set are not present in the training data. We explore different combinations of pre-processing methods, backbone architectures, and post-processing strategies, including text segmentation, patch sampling, and feature aggregation. The results suggest that most configurations perform poorly due to weak capture of low-level visual features, inconsistent patch representations, and high sensitivity to content noise. Still, we identify one end-to-end setup that achieves results comparable to the top-performing system, despite using a simpler design. These findings point to key challenges in building robust end-to-end systems and offer insight into design choices that improve performance in historical document writer identification.

Problem

• Investigating factors affecting end-to-end deep learning for historical writer identification
• Addressing challenges from handwriting diversity and limited labeled samples
• Improving generalization in realistic document-level and zero-shot scenarios

Innovation

• End-to-end deep learning automates feature extraction
• Combines preprocessing backbone and postprocessing strategies
• Identifies robust setup with simplified design